For the soul of Rafael /

Music throughout.

A novel wastewater treatment process: Simultaneous nitrification, denitrification and phosphorus removal

Simultaneous nitrification and denitrification (SND) via the nitrite pathway and anaerobic-anoxic enhanced biological phosphorus removal (EBPR) are two processes that can significantly reduce the COD demand for nitrogen and phosphorus removal. The combination of these two processes has the potential of achieving simultaneous nitrogen and phosphorus removal with a minimal requirement for COD. A lab-scale sequencing batch reactor (SBR) was operated in alternating anaerobic-aerobic mode with a low dissolved oxygen concentration (DO, 0.5 mg/L) during the aerobic period, and was demonstrated to accomplish nitrification, denitrification and phosphorus removal. Under anaerobic conditions, COD was taken up and converted to polyhydroxyalkanoates (PHA), accompanied with phosphorus release. In the subsequent aerobic stage, PHA was oxidized and phosphorus was taken up to less than 0.5 mg/L at the end of the cycle. Ammonia was also oxidised during the aerobic period, but without accumulation of nitrite or nitrate in the system, indicating the occurrence of simultaneous nitrification and denitrification. However, off-gas analysis found that the final denitrification product was mainly nitrous oxide (N2O) not N-2. Further experimental results demonstrated that nitrogen removal was via nitrite, not nitrate. These experiments also showed that denitrifying glycogen.-accumulating organisms rather than denitrifying polyphosphate-accumulating organisms were responsible for the denitrification activity.

Collaborative Filtering with Semantic Neighbour Discovery

Nearest neighbour collaborative filtering (NNCF) algorithms are commonly used in multimedia recommender systems to suggest media items based on the ratings of users with similar preferences. However, the prediction accuracy of NNCF algorithms is affected by the reduced number of items – the subset of items co-rated by both users – typically used to determine the similarity between pairs of users. In this paper, we propose a different approach, which substantially enhances the accuracy of the neighbour selection process – a user-based CF (UbCF) with semantic neighbour discovery (SND). Our neighbour discovery methodology, which assesses pairs of users by taking into account all the items rated at least by one of the users instead of just the set of co-rated items, semantically enriches this enlarged set of items using linked data and, finally, applies the Collinearity and Proximity Similarity metric (CPS), which combines the cosine similarity with Chebyschev distance dissimilarity metric. We tested the proposed SND against the Pearson Correlation neighbour discovery algorithm off-line, using the HetRec data set, and the results show a clear improvement in terms of accuracy and execution time for the predicted recommendations.